Qi Wang (Tsinghua University), Jianjun Chen (Tsinghua University), Jingcheng Yang (Tsinghua University), Jiahe Zhang (Tsinghua University), Yaru Yang (Tsinghua University), Haixin Duan (Tsinghua University)

Session Initiation Protocol (SIP) is a cornerstone of modern real-time communication systems, powering voice calls, text messaging, and multimedia sessions across services such as VoIP, VoLTE, and RCS. While SIP provides mechanisms for authentication and identity assertion, its inherent flexibility poses the risk of semantic ambiguity among implementations that can be exploited by attackers.

In this paper, we present SIPChimera, a novel black-box fuzzing framework designed to systematically identify ambiguity-based identity spoofing vulnerabilities across SIP implementations. We evaluated SIPChimera against six widely used open-source SIP servers—including Asterisk and OpenSIPS—and nine popular user agents, uncovering that attackers could spoof their identity via manipulating identity headers and circumvent authentication. We demonstrate the real-world impact of these vulnerabilities by evaluating five VoIP devices, seven commercial SIP deployments, and three carrier-grade RCS-based SMS platforms. Our experiments show that attackers can exploit these vulnerabilities to perform caller ID spoofing in VoIP calls and send spoofed SMS messages over RCS, impersonating arbitrary users or services. We have responsibly disclosed our findings to affected vendors and received positive acknowledgments. We finally propose remedies to mitigate those issues.

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BINALIGNER: Aligning Binary Code for Cross-Compilation Environment Diffing

Yiran Zhu (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Tong Tang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Jie Wan (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Ziqi Yang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University; Hangzhou High-Tech Zone…

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Select-Then-Compute: Encrypted Label Selection and Analytics over Distributed Datasets...

Nirajan Koirala (University of Notre Dame), Seunghun Paik (Hanyang University), Sam Martin (University of Notre Dame), Helena Berens (University of Notre Dame), Tasha Januszewicz (University of Notre Dame), Jonathan Takeshita (Old Dominion University), Jae Hong Seo (Hanyang University), Taeho Jung (University of Notre Dame)

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Constructive Noise Defeats Adversarial Noise: Adversarial Example Detection for...

Meng Shen (Beijing Institute of Technology), Jiangyuan Bi (Beijing Institute of Technology), Hao Yu (National University of Defense Technology), Zhenming Bai (Beijing Institute of Technology), Wei Wang (Xi'an Jiaotong University), Liehuang Zhu (Beijing Institute of Technology)

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